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Remote sensing has been proven promising in wetland mapping. However, conventional methods in a complex and heterogeneous urban landscape usually use mono temporal Landsat TM/ETM + images, which have great uncertainty due to the spectral similarity of different land covers, and pixel-based classifications may not meet the accuracy requirement. This paper proposes an approach that combines spatiotemporal fusion and object-based image analysis, using the spatial and temporal adaptive reflectance fusion model to generate a time series of Landsat 8 OLI images on critical dates of sedge swamp and paddy rice, and the time series of MODIS NDVI to calculate phenological parameters for identifying wetlands with an object-based method. The results of a case study indicate that different types of wetlands can be successfully identified, with 92.38%. The overall accuracy and 0.85 Kappa coefficient, and 85% and 90% for the user's accuracies of sedge swamp and paddy respectively. [ABSTRACT FROM AUTHOR]

Wetlands play irreplaceable key roles in ecological and environmental procedures. To make effective conservation and management, it is essential to understand the wetlands’ distribution and changes. In this study, an approach based on decision rules algorithm in conjunction with maximum likelihood classification is proposed for coastal wetland mapping using multi-temporal remotely sensed imagery and ancillary geospatial data. As a case study, Multi-temporal Advanced Visible and Near Infrared Radiometer type 2 images acquired by Japanese Advanced Land Observation Satellite are analysed to investigate the seasonal change pattern of coastal wetlands in Washington State, USA. Geospatial data, including Digital Elevation Model and spatial neighbourhood knowledge, are further integrated to characterize wetland features and discriminate classes within a certain elevation ranges. The final result is a refined coastal wetland map with 15 land cover categories. Preliminary evaluation of the final result shows that the proposed approach is effective in coastal wetland mapping. [ABSTRACT FROM PUBLISHER]

This study integrates data from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Light Detection and Ranging (LiDAR) to map the spatial configuration of coastal wetland landscapes. We test data sources in their ability to capture marsh features and sources are combined to improve wetland characterizations. The complex ecosystem characteristics of the Wells Maine National Estuarine Research Reserve marshes and surrounding areas provide an ideal study site. The results of this study suggest that ASTER visible, near and shortwave infra red spectral bands combined with LiDAR last return signals provide accurate wetland cover maps categorized in broad cover classes. LiDAR contributions are important in areas of elevation and structure variability where multispectral data are unable to distinguish fine scale variations in vegetation and water signals. ASTER's cost effectiveness and spectral range offer a data-rich alternative to expensive high spatial resolution imagery typically used in multispectral and LiDAR combined studies. [ABSTRACT FROM PUBLISHER]